Multiple Virtual Network Functions Research Articles

Bidirectional Service Function Chain Embedding for Interactive Applications in Mobile Edge networks

Bidirectional service function chain (BSFC) consists of multiple virtual network functions (VNFs). Through VNF deployment and link mapping, BSFCs can be embedded into resource-constrained mobile edge networks to provide low-latency network function services to users participating in interactive applications such as multi-player online games. Data from these users are routed through BSFCs to the edge node where the application is located for interaction and then returned to the users through the BSFCs, thus enabling synchronization among multiple users. However, the edge nodes or links have limited computing or bandwidth resources to serve only a fraction of users simultaneously. Therefore, the embedding decisions among different users can affect each other. In this paper, we propose a novel BSFC embedding strategy for interactive applications with the goal of minimizing computing and bandwidth resources while satisfying users' latency requirements. We first model the BSFC embedding problem as an integer nonlinear programming problem. Then, by closely examining the complexity of the problem, we propose a distributed algorithm based on game theory. We theoretically analyze the properties of the proposed algorithm and show that it can obtain a solution with a worst-case performance bound. Finally, extensive experiments show that the proposed algorithm outperforms several existing algorithms.

Availability‐aware virtual network function placement based on multidimensional universal generating functions

AbstractNetwork function virtualization (NFV) implements network functions as software, which enables flexible, resource‐efficient, and rapid provision of network services. In NFV, network services are realized by the service function chain (SFC), which is a chain of virtual network functions (VNFs) linked in the proper order. Both availability and deployment cost are key concerns for network operators providing network services as SFC. This paper proposes a flexible VNF placement model on a per‐VNF instance basis that minimizes deployment costs while satisfying availability requirements that may be placed on SFC. This paper uses a multidimensional universal generating function (MUGF) method, which is a multistate system analysis method, to compute the availability of a multistate SFC system with multiple VNFs coexisting on a server. The MUGF method calculates the performance of the entire SFC by combining the performance of servers as determined by applying a continuous‐time Markov chain. To reduce the time to compute the SFC availability, we introduce operators to be applied to MUGF and develop an availability computing method. In addition, a heuristic algorithm for determining VNF placement targeting the lowest deployment cost possible while meeting availability requirements is presented. Numerical results show that the proposed model obtains VNF placement with lower cost than the conventional model in all examined cases. The proposed model achieves VNF placement at 58.5%–75.0% of the deployment cost of the conventional model for the same SFC availability requirements.

NEST: Optimal deploying DAG-SFCs to maximize the flows wholly served in the network edge

Compared with the dedicated hardware-based middle-boxes, Network Function Virtualization (NFV) makes network services more flexible, scalable, and cost-efficient by replacing the network functions, e.g., Firewalls, Deep Packet Inspections (DPI), Load Balancers, by Virtual Network Functions (VNFs) running on general-purpose commercial servers. Multiple VNFs usually chain up to formulate Service Function Chains (SFC) to enable complex network services. With the exploitation of VNF parallelism, SFC with parallel VNFs (Para-SFC) is proposed to reduce the SFC execution delay. However, the inner-structure of a Para-SFC is usually complex and a Para-SFC may have multiple legitimate topologies. DAG-SFC is one of the basic sharping methods for standardizing the topology of Para-SFCs. To adapt to the fast development of edge computing, it is critical to find an optimal deployment of Para-SFCs in hybrid edge-and-cloud network with the purpose of maximizing the amount of the flows completely serviced in the edge. In this paper, we first analyze the target problem via integer program modeling and prove its NP-hardness. Then, we propose a heuristic algorithm named NEST, which is based on the combination of maximum spanning tree and Next Fit. With extensive evaluations, we demonstrate that, compared with the benchmark methods, NEST can achieve up to respective 14% and 37% performance gain on DAG-SFC edge deployment maximization while can always realize (up to respective 276% and 305%) higher efficiency of link usage.

A reinforcement learning method for scheduling service function chains with multi-resource constraints

Traditional networks are usually equipped with many dedicated middleboxes to provide various network services. Though these hardware-based devices certainly improve network performance, they are usually expensive and difficult to upgrade. To overcome this shortcoming, network function virtualization (NFV), which accomplishes network services in the form of virtual network functions (VNF) has been presented. Compared to middleboxes, the VNFs are easy to deploy and migrate. Usually, multiple VNFs are chained in a specified order as a service function chain (SFC) to serve a given flow. There are many works to schedule SFCs to minimize the average flow completion time. However, they only consider single resource limitation. In this paper, we are committed to addressing the problem of multi-resource SFC scheduling (MR-SFCS) and minimizing the average flow completion time. We formulate this problem with an Integer Linear Programming (ILP) model and prove its NP-hardness. To well tackle this problem, we propose an approach based on deep reinforcement learning (DRL), which has specific reward design and state representations. Besides, we extend the offline approach to online SFC scheduling. The experiment results demonstrate that our DRL method can significantly reduce the average flow completion time and achieves a cost saving of 69.07% against the benchmark method.

Dynamic SFC placement scheme with parallelized SFCs and reuse of initialized VNFs: An A3C-based DRL approach

Mobile Edge Computing (MEC) is a well-known network architecture that extends cloud computing to the network edge. Compared with cloud computing, Network Function Virtualization (NFV) can provide flexible services in MEC for mobile users. Virtual Network Functions (VNFs) have emerged as software-based hardware middleboxes by NFV technology to host real-time applications. Basically, the combination of multiple VNF instances is defined as a Service Function Chain (SFC), which can provide dynamic service requirements in the MEC. Despite the rapid growth of MEC and the widespread support of service providers for SFC, many issues are still challenging and need to be addressed. In MEC scenarios with limited resources, the effective placement of SFCs with the aim of resource efficiency remains a challenging problem. Motivated by the scalability shortcomings of existing schemes to solve dynamic placement of SFCs, we propose Deep Reinforcement Learning (DRL)-based approaches to solve this problem, i.e., Asynchronous Advantage Actor-Critic (A3C). The proposed scheme is based on the reuse of initialized VNFs to improve the Quality of Service (QoS), which is developed with the aim of maximizing the long-term cumulative reward. In addition, a parallel processing approach of SFCs is included in the proposed scheme, which can split the traffic in each flow into sub-flows. This shares the processing load by instantiating duplicate instances of each VNF type in the SFC. The simulation results guarantee the efficiency of the proposed scheme and improves the average performance between 6% and 24% compared to the state-of-the-art clustering methods.

An Online Algorithm for Virtualized Network Function Placement in Mobile Edge Industrial Internet of Things

Mobile edge Industrial Internet of Things (MEIIoT) is composed of Industrial Internet of Things (IIoT) and mobile edge computing, which is currently a new type of IIoT. MEIIoT has the characteristics of large scale and strong dynamics (e.g., network topology or number of IIoT devices would change from time to time). The placement of virtualized network functions (VNFs) in MEIIoT refers to placing multiple network functions (e.g., motion analyzer and video processor) on edge nodes in a form of software instances, so that IIoT devices can flexibly obtain services of these VNFs. However, an edge node can only be placed a small number of VNFs, because of its limited storage and computing resources. Therefore, if an IIoT device requires multiple VNFs, it needs to transmit its data to access several edge nodes, which would cause high delay. How to optimally place all the VNFs on edge nodes in MEIIoT, so that the whole access delay for all IIoT devices that requiring VNFs is minimized, is a challenging problem. In this article, we design an online placement algorithm. First, we decompose a long-term VNFs optimization problem into a series of one-shot optimization problems. Second, we formulate these one-shot problems into integer nonlinear programming problems, and prove that they are NP-hard. To overcome this hardness, we then propose a heuristic algorithm. Finally, we carried out extensive experiments with real-world datasets to validate the efficacy of our proposed solution.

Offloading in P4 Switch Integrated with Multiple Virtual Network Function Servers

Offloading in P4 Switch Integrated with Multiple Virtual Network Function Servers

Edge intelligence for service function chain deployment in NFV-enabled networks

With evolution of network function virtualization (NFV), network services can be provided as service function chains (SCs), each consisting of multiple virtual network functions (VNFs). The deployment of SCs including placement of VNF instances and virtual links connecting these functions, onto the substrate physical network is a critical issue which significantly affects the performance of the offered network services. Due to the unpredictable traffic and network state variations, as well as diverse quality of service (QoS) requirements, an online SCs deployment approach is needed to cope with different service requests and real-time network traffics. In this paper, we employ edge intelligence using a distributed deep reinforcement learning approach to deploy SCs in order to jointly balance the load on the physical nodes and links in the edge environments. The evaluation results show that the proposed approach outperforms state-of-the-art algorithms in terms of minimizing the drop rate of the incoming service chain requests. In addition, the proposed approach is able to rapidly deploy service flows even in the large real-world network typologies.

Hop-by-hop bandwidth allocation and deployment for SFC with end-to-end delay QoS guarantees

Network function virtualization and 5G network slicing technology can provide a more resilient and manageable Internet by deploying various requests on different slices. In this paper, we propose a hop-by-hop bandwidth allocation framework for service function chains (SFCs) to guarantee delay QoS constraints of requests. A end-to-end (E2E) SFC contains multiple virtual network functions (VNFs), which is constructed as a tandem queueing system. The arrival is modeled as an interrupted bernoulli process (IBP) or two-aggregation Markov modulated bernoulli process (2A-MMBP) to represent the traffic of typical services, data or video call. And the effective bandwidth (EB) formulas for these intricate Markovian processes are derived. Then we use EB/ effective capacity (EC) theory to allocate the bandwidth for SFCs hop by hop and deduce the service probabilities of the nodes. On the basis of EB/EC bandwidth allocation, the departure process is fitted into an IBP or a MMBP and then becomes the arrival of the next hop, which is beneficial to engineering implementation. The novel delay evaluation metrics, E2E packet delay and delay violation probability, are developed to assess the performance of SFC deployment. We formulate the SFC deployment as an integral linear programming (ILP) problem with the objective of minimizing the total cost. Finally, a heuristic algorithm called hop-by-hop bandwidth allocation and SFC deployment (HBASD) is designed to solve this problem. Simulation results show that compared with the up-to-date methods, our algorithm can guarantee the QoS requirements of E2E delay for services and save resources.

Priority-awareness VNF migration method based on deep reinforcement learning

By decoupling the software function on hardware devices, Network Function Virtualization (NFV) provides a new service architecture named Service Function Chain (SFC), which combines multiple Virtual Network Functions (VNFs) in a specific order. In order to improve the reliability and Quality of Service (QoS) of network services, VNF migration provides an effective solution for this requirement. However, traditional migration methods lack an appropriate VNF selection mechanism and consider a single network state. Moreover, how to determine the accurate migration location dynamically with machine learning is challenging. This paper proposed a novel PAVM algorithm for reliable and optimal VNF migration, which distinguishes two kinds of VNF migration schemes for network congestion and node failure states respectively, and a node and VNF priority-awareness mechanism is designed, which can select appropriate VNF on suitable node to migrate. Moreover, PAVM utilizes deep reinforcement learning algorithm to choose target node location of VNF migration, which jointly utilizes delay, load balancing of network as feedback factors to optimize the QoS. The experimental results indicate that compared with other three benchmark algorithms, PAVM can effectively reduce the transmission delay and improve node and link load balancing after the VNF migration.

Federated Multi-Discriminator BiWGAN-GP Based Collaborative Anomaly Detection for Virtualized Network Slicing

Virtualized network slicing allows a multitude of logical networks to be created on a common substrate infrastructure to support diverse services. A virtualized network slice is a logical combination of multiple virtual network functions, which run on virtual machines (VMs) as software applications by virtualization techniques. As the performance of network slices hinges on the normal running of VMs, detecting and analyzing anomalies in VMs are critical. Based on the three-tier management framework of virtualized network slicing, we first develop a federated learning (FL) based three-tier distributed VM anomaly detection framework, which enables distributed network slice managers to collaboratively train a global VM anomaly detection model while keeping metrics data locally. The high-dimensional, imbalanced, and distributed data features in virtualized network slicing scenarios invalidate the existing anomaly detection models. Considering the powerful ability of generative adversarial network (GAN) in capturing the distribution from complex data, we design a new multi-discriminator Bidirectional Wasserstein GAN with Gradient Penalty (BiWGAN-GP) model to learn the normal data distribution from high-dimensional resource metrics datasets that are spread on multiple VM monitors. The multi-discriminator BiWGAN-GP model can be trained over distributed data sources, which avoids high communication and computation overhead caused by the centralized collection and processing of local data. We define an anomaly score as the discriminant criterion to quantify the deviation of new metrics data from the learned normal distribution to detect abnormal behaviors arising in VMs. The efficiency and effectiveness of the proposed collaborative anomaly detection algorithm are validated through extensive experimental evaluation on a real-world dataset.

PPTMon: Real-Time and Fine-Grained Packet Processing Time Monitoring in Virtual Network Functions

By softwarizing the legacy network functions, Network Function Virtualization (NFV) allows rapid development and deployment of network services as well as simplicity and flexibility in network operations and management. Monitoring the performance characteristics of Virtual Network Functions (VNFs), particularly packet processing time, is important to ensure that VNFs are operating correctly with desired performance. This is especially crucial for low-latency network services. In this paper, we present Packet Processing Time Monitoring (PPTMon), a real-time, fine-grained, and end-to-end solution for VNF packet processing time monitoring. PPTMon can provide per-hop monitoring for a single VNF as well as end-to-end monitoring for multiple VNFs in a service function chain. PPTMon allows monitoring in both sampling and continuous fashions. Continuously monitoring every packet may greatly degrade the performance of the VNFs and generate a huge amount of monitoring data. PPTMon’s event-filtering algorithm effectively filters out non-important data and reduces the performance overhead. PPTMon processes packets in-stack by embedding timestamp information directly into the packets, thus further reducing the effect on the VNF performance. PPTMon is implemented on top of extended Berkeley Packet Filter (eBPF) – a Linux framework that allows high-speed packet processing. Our experiment results shows that PPTMon can monitor VNF packet processing time with high accuracy and low impact on performance.

Impact of Processing-Resource Sharing on the Placement of Chained Virtual Network Functions

Network Function Virtualization (NFV) provides higher flexibility for network operators and reduces the complexity in network service deployment. Using NFV, Virtual Network Functions (VNF) can be located in various network nodes and chained together in a Service Function Chain (SFC) to provide a specific service. Consolidating multiple VNFs in a smaller number of locations would allow decreasing capital expenditures. However, excessive consolidation of VNFs might cause additional latency penalties due to processing-resource sharing, and this is undesirable, as SFCs are bounded by service-specific latency requirements. In this paper, we identify two different types of penalties (referred as "costs") related to the processingresource sharing among multiple VNFs: the context switching costs and the upscaling costs. Context switching costs arise when multiple CPU processes (e.g., supporting different VNFs) share the same CPU and thus repeated loading/saving of their context is required. Upscaling costs are incurred by VNFs requiring multi-core implementations, since they suffer a penalty due to the load-balancing needs among CPU cores. These costs affect how the chained VNFs are placed in the network to meet the performance requirement of the SFCs. We evaluate their impact while considering SFCs with different bandwidth and latency requirements in a scenario of VNF consolidation.

VNF-based network service consistent reconfiguration in multi-domain federations: A distributed approach

The Network Function Virtualization (NFV) virtualizes the network appliances — such as routers and firewalls — with software running on commercial off-the-shelf servers. In NFV specification, Network Services (NS) are composed of multiple Virtual Network Functions (VNF) enabling elastic and finer lifecycle management operations such as scaling. For adapting resources to the VNF-based network services evolving context, these operations require to execute, on the fly, a reconfiguration plan supervised by a central orchestrator. This is the single-domain reconfiguration, where the orchestrator has global up-to-date information, ensuring the correct execution of the lifecycle management operations. Moreover, in practice, NS can be implemented by composing VNFs in a cross-domain schema called a multi-domain federation. In this case, the reconfiguration is more challenging since there are multiple orchestrators, one by domain, that manage collaboratively shared network services. Sharing network services creates functional and non-functional dependencies among these services that must be considered for ensuring the consistency of the lifecycle operations. The consistent reconfiguration of shared network services in distributed multi-domain federations is called the NFV Dependent Reconfiguration problem. However, despite being identified as an important challenge by the NFV community, no related solution has yet been proposed. In this paper, we focus on the NFV Dependent Reconfiguration problem. We introduce a distributed approach to guarantee consistency during dependent reconfiguration. The approach is composed of a distributed multi-domain model that establishes the interactions among the federation's entities, and a causally-consistent distributed orchestration algorithm based on such a model. We verify the algorithm viability using, as a case study, the scaling of shared VNF-based network services as defined by the current NFV standard architecture. To the best of our knowledge, the proposed distributed orchestration approach is the first that supports the consistent execution of dependent reconfiguration operations for VNF-based network services.

Blockchain-Enabled SLA Compliance for Crowdsourced Edge-Based Network Function Virtualization

Network function virtualization (NFV) allows edge devices to host different types of virtual network functions (VNFs) and support Computing in the Network. Therefore, an edge device can abstract multiple VNFs of different infrastructure providers (InPs) as services and serve them at different times to act as a tenant for different InPs. Deploying edge devices is expensive and difficult to manage. Hence, crowdsourced edge devices can be used. To ensure the quality and availability of the services and the privacy of sensitive data of the service stakeholders, it is extremely important to establish a service level agreement (SLA) between an edge device owner and an InP. For monitoring of SLA compliance and enforcing accountability, a trusted platform is required. The traditional NFV management authority cannot be trusted due to its centralized characteristics. In this article, we propose a blockchain-powered framework for the SLA compliance of edge devices offering VNFs as services and introduce a smart contract-driven framework to establish trust in edge-based NFV. We analyze the performance of the proposed framework in the context of a private blockchain network.

Reliable Placement of Service Function Chains and Virtual Monitoring Functions With Minimal Cost in Softwarized 5G Networks

Network Functions Virtualization (NFV) allows softwarization of network functions and enables to run network functions as Virtual Network Function (VNF) instances on top of the cloud infrastructure. In softwarized 5G networks, communication services can be realized through Service Function Chains (SFCs) in which multiple VNFs are connected in a sequential order as per the requirements, which offer flexibility, agility, and dynamic management and orchestration of networks. However, softwarization and cloudification of networks using NFV introduce new challenges in terms of reliability and availability due to software bugs, hardware failures, malfunction of VNFs, and service degradation. In this article, we first explore latency-aware and reliable SFC placement to meet the requirements of users and enhance the reliability of SFCs from VNF failures. Then, we focus on reliable placement of virtual monitoring functions at proximal locations of VNFs in order to identify and mitigate service degradation and security-related issues in the network. To this end, we formulate the problems as Integer Linear Programming (ILP) problems to minimize the total deployment cost, and show that they are NP-hard. To overcome the high computational complexity of the ILP problems, we propose novel heuristic algorithms based on complex network theory to provide near-optimal solutions in polynomial time for large input instances. By extensive simulations, we show that our proposed algorithms provide near-optimal solution (optimality gap is 5%) in a real-world network topology.

Design and Implementation of Virtual Security Function Based on Multiple Enclaves

Network function virtualization (NFV) provides flexible and scalable network function for the emerging platform, such as the cloud computing, edge computing, and IoT platforms, while it faces more security challenges, such as tampering with network policies and leaking sensitive processing states, due to running in a shared open environment and lacking the protection of proprietary hardware. Currently, Intel® Software Guard Extensions (SGX) provides a promising way to build a secure and trusted VNF (virtual network function) by isolating VNF or sensitive data into an enclave. However, directly placing multiple VNFs in a single enclave will lose the scalability advantage of NFV. This paper combines SGX and click technology to design the virtual security function architecture based on multiple enclaves. In our design, the sensitive modules of a VNF are put into different enclaves and communicate by local attestation. The system can freely combine these modules according to user requirements, and increase the scalability of the system while protecting its running state security. In addition, we design a new hot-swapping scheme to enable the system to dynamically modify the configuration function at runtime, so that the original VNFs do not need to stop when the function of VNFs is modified. We implement an IDS (intrusion detection system) based on our architecture to verify the feasibility of our system and evaluate its performance. The results show that the overhead introduced by the system architecture is within an acceptable range.

Deep Reinforcement Learning Based Migration Mechanism for Service Function Chain in Operator Networks

To improve the service experience provided by the operator network, this paper studies the online migration of Service Function Chain(SFC). Based on the Markov Decision Process(MDP), modeling analysis is performed on the migration of multiple Virtual Network Functions(VNF) in SFC. By combining reinforcement learning and deep neural networks, a double Deep Q-Network(double DQN) based service function chain migration mechanism is proposed. This method can make online migration decisions and avoid over-estimation. Experimental result shows that when compared with the fixed deployment algorithm and the greedy algorithm, the double DQN based SFC migration mechanism has obvious advantages in end-to-end delay and network system revenue, which can help the mobile operator to improve the quality of experience and the efficiency of resources usage.

VNF Chaining Performance Characterization under Multi-Feature and Oversubscription Using SR-IOV

Network Function Virtualization (NFV) has revolutionized the way network services are offered, leading Enterprise and Service Providers to increasingly adapt their portfolio of network products in order to reap the benefits of flexible network service deployment and cost reduction promises. With this method, network services are offered in the form of software images instead of dedicated hardware. However, NFV presents several challenges, including standard networking challenges (e.g., security, resilience, and availability), management and orchestration challenges, resource allocation challenges, and performance trade-off challenges of using standard x86 servers instead of dedicated and proprietary hardware. The first three challenges are typical challenges found in virtualization environments and have been extensively addressed in the literature. However, the performance trade-off challenge can be the most impactful when offering networking services, negatively affecting the throughput and delay performance achieved. Thus, in this paper, we investigate and propose several configurations on a virtualized system for increasing the performance in terms of throughput and delay while chaining multiple virtual network functions (VNFs) in case of an undersubscribed and oversubscribed system, where the resource demands exceeds the physical resource capacity. Specifically, we use the Single Root Input Output Virtualization (SR-IOV) as our Input/Output (I/O) technology, and analyze the attainable throughput and delay when running multiple chained VNFs in a standard x86 server under various resource footprints and network features configurations. We show that the system throughput and delay in a multi-chained environment, offering multiple features, and under oversubscription can affect the overall performance of VNFs.

Extendable NFV-Integrated Control Method Using Reinforcement Learning

Network functions virtualization (NFV) enables telecommunications service providers to realize various network services by flexibly combining multiple virtual network functions (VNFs). To provide such services, an NFV control method should optimally allocate such VNFs into physical networks and servers by taking account of the combination(s) of objective functions and constraints for each metric defined for each VNF type, e.g., VNF placements and routes between the VNFs. The NFV control method should also be extendable for adding new metrics or changing the combination of metrics. One approach for NFV control to optimize allocations is to construct an algorithm that simultaneously solves the combined optimization problem. However, this approach is not extendable because the problem needs to be reformulated every time a new metric is added or a combination of metrics is changed. Another approach involves using an extendable network-control architecture that coordinates multiple control algorithms specified for individual metrics. However, to the best of our knowledge, no method has been developed that can optimize allocations through this kind of coordination. In this paper, we propose an extendable NFV-integrated control method by coordinating multiple control algorithms. We also propose an efficient coordination algorithm based on reinforcement learning. Finally, we evaluate the effectiveness of the proposed method through simulations.